In this section, we empirically evaluate the performance of proposed unlearning method in the FL settings.
We report experiments on CIFAR10 and CIFAR100 datasets [1]} with ResNet and [2]} VGG [3]} models.
We choose these datasets and models because they are sufficiently standard and general in image classification tasks, thus proving that our method is general instead only valid for specific image tasks, such as facial image classification.
Specially, CIFAR10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
CIFAR100 dataset is just like the CIFAR10, except it has 100 classes containing 600 images each.
On both CIFAR10 and CIFAR100, we forget the information of entire class from the deep CNN models, compared to two baselines.
